Original Research Paper
S. Ashraf; T. Ahmed; Z. Aslam; D. Muhammad; A. Yahya; M. Shuaeeb
Articles in Press, Accepted Manuscript, Available Online from 20 June 2020
Abstract
The quick response time and the coverage range are the crucial factors by which the quality service of a wireless sensor network can be acknowledged. In some cases, even networks possess sufficient available bandwidth but due to coverage tribulations, the customer satisfaction gets ...
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The quick response time and the coverage range are the crucial factors by which the quality service of a wireless sensor network can be acknowledged. In some cases, even networks possess sufficient available bandwidth but due to coverage tribulations, the customer satisfaction gets down suddenly. The increasing number of nodes directly is neither a canny solution to overcome the coverage problem nor a cost-effective. In fact, by changing the positions of the deployed node sagaciously can resolve the coverage issue and seems a cost-effective solution. Therefore, keeping all circumstances, a Depuration based Efficient Coverage Mechanism (DECM) has been developed. This algorithm suggests the new shifting positions for previously deployed sensor nodes to fill the coverage gap. Indeed, it’s a redeployment process which accomplished in two rounds. The first round avails the Dissimilitude Enhancement Scheme (DES), which searches the node to be shifted at new positions. The second round controls the unnecessary movement of the sensor nodes by the Depuration mechanism thereby the distance between previous and new positions is reduced. In addition, the factors like loudness, pulse emission rate, maximum frequency, and sensing radius are meticulously explored during simulation rounds conducted by MATLAB. The performance of DECM has been compared with superlative algorithms i.e., Fruit Fly Optimization Algorithm (FOA), Particle Swarm Optimization (PSO) , and Ant Colony Optimization (ACO) in terms of mean coverage range, computation time, standard deviation, and network energy diminution. According to the simulation results, the DECM has achieved more than 98% coverage range, with a trivial computation time of nearly 0.016 seconds as compared to FOA, PSO, and ACO.
Original Research Paper
Data Preprocessing
F. Tabib Mahmoudi; A. Karami
Articles in Press, Accepted Manuscript, Available Online from 20 June 2020
Abstract
Background and Objectives: Pan-sharpening algorithms integrate the spectral capabilities of the multispectral imagery with the spatial details of the panchromatic one to obtain a product with confident spectral and spatial resolutions. Due to the large diversities in the utilized pan-sharpening algorithms, ...
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Background and Objectives: Pan-sharpening algorithms integrate the spectral capabilities of the multispectral imagery with the spatial details of the panchromatic one to obtain a product with confident spectral and spatial resolutions. Due to the large diversities in the utilized pan-sharpening algorithms, occurring spatial and spectral deviations in their results should be recognized by performing the quantitative assessment analysis. Methods: In this research, the pan-sharpened images from PCA, IHS, and Gram-Schmidt transformation based algorithms are evaluated for the multi-spectral and panchromatic images fusion of Landsat-8 OLI sensor (medium scale resolution satellite) and WorldView-2 (high-resolution satellite). Quantitative analysis is performed on the pan-sharpened products based on the Per-Pixel Deviation (PPD) measure for spectral deviation analysis and high-pass filter and edge extraction measures for analyzing the spatial correlations. Moreover, entropy and standard deviation quantitative evaluation measures are also utilized based on the pan-sharpened image content. Results: Quantitative analysis represents that increasing the spatial resolution of the utilized remote sensing data has direct impacts on the spectral, spatial, and content-based characteristics of the generated Pan-sharpened products. Gram-Schmidt transformation based pan-sharpening method has the least spectral deviations in both WorldView-2 and Landsat-8 satellite images. But, the amount of spectral, spatial and content-based quantitative measures of PCA and IHS are changing with various spatial resolutions. Conclusion: it can be said that Gram-Schmidt pan-sharpening method has the best performance in both medium-scale and high-resolution data sets based on the spectral, spatial, and content quantitative evaluation results. The IHS pan-sharpening method has better performance than the PCA method in Landsat-8 OLI data. But, by increasing the spatial resolution of the data, PCA generates pan-sharpened products with better spectral, spatial, and content based quantitative evaluation results.
Original Research Paper
Data Mining
Y. Rohani; Z. Torabi; S. Kianian
Articles in Press, Accepted Manuscript, Available Online from 20 June 2020
Abstract
Background and objectives: Prediction of students' academic performance is essential for systems emphasizing students' greater success. The results can largely lead to increase in the quality of the educating and learning. Through the application of data mining, useful and innovative patterns can be ...
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Background and objectives: Prediction of students' academic performance is essential for systems emphasizing students' greater success. The results can largely lead to increase in the quality of the educating and learning. Through the application of data mining, useful and innovative patterns can be extracted from the educational data. Methods: In this paper, a new metaheuristic algorithm, combination of simulated annealing and genetic algorithms, is proposed for predicting students’ academic performance in educational data mining. Although metaheuristic algorithms are one of the best options for discovering the hidden relationships between data in data science, they do not separately perform well in accurate prediction of students’ academic performance. Therefore, the proposed method integrates the advantages of both genetic and simulated annealing algorithms. The genetic algorithm is applied to explore new solutions, while simulated annealing is used to increase the exploitation power. By using this combination, the proposed algorithm has been able to predict the students’ academic performance with high accuracy. Results: The efficiency of the proposed algorithm is evaluated on five different educational data sets, including two data sets of students of Shahid Rajaee University of Tehran and three online educational data sets. Conclusion: Our experimental results show 1.09%-24.39% and 0.29%-6.57% accuracy improvement of the proposed algorithm in comparison to the four similar metaheuristic and five popular classification methods respectively.
